Joint Neural Collaborative Filtering for Recommender Systems
arXiv:1907.03459
Abstract
We propose a J-NCF method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modeling captures non-linear user-item interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. J-NCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization, which takes both implicit and explicit feedback, point-wise and pair-wise loss into account. Experiments on several real-word datasets show significant improvements of J-NCF over state-of-the-art methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR@10. NDCG@10 improvements are 12.42%, 14.24% and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of J-NCF. Our experiments show that the J-NCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to state-of-the-art baselines.
30 pages
References in corpus (7)
- Adam: A Method for Stochastic Optimization
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
- DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
- Ask the GRU: Multi-Task Learning for Deep Text Recommendations
- Wide & Deep Learning for Recommender Systems
- Scalable Recommendation with Poisson Factorization